To verify the adaptability of the pruning methods [1]}, [2]} in the generator network, we visualize the pruning ratio of each layer in the Pix2Pix generator in Figure REF .
We find that the pruning ratio of each layer likes an inverted letter “U” as the number of network layers deepens.
Pix2Pix generator takes U-Net as the backbone network, so the first/last few layers play a direct role in generating real-like fake images.
The output feature map size is only 1\(\times \) 1 in the most intermediate network layer, which has little effect on the final 256\(\times \) 256 image.
In conclusion, the pruning methods [1]}, [2]} can better identify the important convolution kernel of the generator.
